In the field of business intelligence (BI), it is conceivable to support analyst decision-making by presenting information satisfying a condition designated by the analyst. An example thereof is indicated below.
FIG. 16 is a schematic diagram illustrating various tables included in a database. FIG. 16 illustrates a purchase table, a customer master, a product master, and a store master. In the purchase table, a customer ID, a product ID, a store ID, and a purchase quantity are associated with each other so as to indicate which customer purchased how many of which product at which store. The customer master indicates an attribute value of a particular customer (age in the example illustrated in FIG. 16) for each customer ID. The product master indicates an attribute value of a particular product (a product category in the example illustrated in FIG. 16) for each product ID. The store master indicates the attribute of a particular store (in the example illustrated in FIG. 16, a district where the store is located) for each store ID.
An example is indicated below in which a BI tool (illustration is omitted) extracts information satisfying a condition designated by an analyst from the database exemplified in FIG. 16. It is supposed that the analyst wishes to know a “purchase quantity of jackets purchased by people in twenties at Kawasaki”. In this case, the analyst designates a condition of “twenties” with respect to the customer, designates a condition of “jacket” with respect to the product, and designates a condition of “Kawasaki” with respect to the store. Then, the BI tool narrows down the customer ID of a customer corresponding to “twenties” from the customer master, narrows down the product ID of a product corresponding to “jacket” from the product master, and narrows down the store ID of a store corresponding to “Kawasaki” from the store master. Subsequently, the BI tool specifies a row including a combination of the above-mentioned customer ID, product ID, and store ID from the purchase table. In this example, rows A and C in the purchase table are specified. Then, the BI tool calculates the sum of the purchase quantity in each of these rows to present to the analyst.
In FIG. 16, although information on purchase time is not included in the purchase table, it is also conceivable that the analyst requests to display a change in purchase quantity per period (for example, every month) in the purchase table, including the purchase time. In this case, it is also conceivable that the BI tool calculates the “purchase quantity of jackets purchased by people in twenties at Kawasaki” for each month as described above and displays a graph exemplified in FIG. 17. FIG. 17 exemplifies a monthly change in “purchase quantity of jackets purchased by people in twenties at Kawasaki”. The analyst can check information visualized as exemplified in FIG. 17 to make the decision.
Meanwhile, Patent Literature 1 describes an analysis environment in which a relational database server and an online analytical processing (OLAP) server are connected through a network.